1. Field of the Invention
The present invention pertains to the diagnosis of defects in ciliary motion that can lead to sinopulmonary disease, and in particular, to systems and methods for automatically classifying ciliary motion as normal or abnormal using video images of the ciliary motion and decomposing the ciliary motion into constituent motion elements, such as, without limitation, rotation and deformation.
2. Description of the Related Art
Cilia are microtubule-based hair-like projections of the cell that can be motile or immotile, and in humans are found on nearly every cell of the body. Diseases known as ciliopathies with disruption of nonmotile or motile cilia function can result in a wide spectrum of diseases. In primary ciliary dyskinesia (PCD), cilia in the airway that normally beat in synchrony to mediate mucus clearance can exhibit dyskinetic motion or become immotile, resulting in severe sinopulmonary disease. As motile cilia are also required for left-right patterning, PCD patients can exhibit mirror symmetric organ placement, such as in Kartagener's syndrome, or randomized left-right organ placement, such as in heterotaxy. Patients with congenital heart disease (CHD) and heterotaxy exhibit a high prevalence of ciliate motion (CM) defects similar to those seen with PCD. CM defects have also been associated with increased respiratory complications and poor postsurgical outcomes. Similar findings were observed in patients with a variety of other CHD, including transposition of the great arteries (TGA). Diagnosing patients with CM abnormalities early may provide the clinician with opportunities to institute prophylactic respiratory therapies to prevent the need for surgical treatments.
Current methods for assessing CM rely on a combination of tools comprising a “diagnostic ensemble.” One of these tools entails the use of video microscopy for CM analysis of nasal brush biopsies. Ciliary beat frequency (CBF) can be computed from these videos, but it has low sensitivity for abnormal CM. Furthermore, CBF does not capture the broad distribution of frequencies present in ciliary biopsies. Clinicians often employ visual assessment of ciliary beat pattern to augment CBF measurements. However, this relies on reviewer experience and is therefore highly subjective and error-prone. Electron microscopy (EM), considered one of the most reliable methods of the ensemble, cannot identify PCD patients who present without ultra-structural defects. Finally, it is difficult to compare results of the diagnostic ensemble in cross-institutional studies. Quantifying CM requires an objective, sensitive, and verifiable method. Computational methods, for example, can be trained to detect different types of motion and small changes in beat frequencies for identifying CM defects, and either present them in a quantitative format, or classify the motion phenotypes with greater precision and objectivity to make them suitable for clinical diagnosis.
Thus, there is a need for a system and method for automatically classifying ciliary motion as normal or abnormal in order to effectively and efficiently diagnose CM defects and thereby identify patients at risk for sinopulmonary disease.